DEA Window Analysis Efficiency Measurement of Selected Nigerian Bank

This study analyzes the performance efficiency of six selected banks in Nigeria for the period 2010 – 2016. DEA window analysis was employed to establish the performance efficiency of the selected banks. The analysis is based on panel data for the period under review. The result of the DEA window analysis for the reviewed period showed that the average efficiency scores under constant returns to scale ranged from 84% to 91%. Under the variable returns to scale, the average efficiency scores ranged from 91% to 95%. The average inefficiency of the selected Nigeria commercial banks under the constant returns to scale model was in the range 9 – 16%. This inefficiency could be attributed to the excess of customers deposits on the balance sheet of the selected banks. The average scale efficiency for the banks was 93%. Guaranty Trust Bank was the most efficient bank on all measures. United Bank for Africa was the most inefficient bank under constant returns to scale and variable returns to scale. It was however, more scale efficient than three other banks, an indication that its inefficiency cannot be attributed to inappropriate scale size.

Determinants of hospital inefficiency. The case of Polish county hospitals

Local hospitals play a crucial role in the healthcare system. In this study, the efficiency of Polish county hospitals is assessed by considering characteristics of hospitals that may determine their performance, such as the form of ownership, size, and staff structure. The main goal was to analyze the effect of three possible determinants on efficiency: ownership, the presence of an Emergency Department, and the presence of an Intensive Care Unit. The study covered different subgroups of hospitals and different approaches of inputs and outputs. An input-oriented radial super-efficiency DEA model under variable returns to scale was used for the efficiency analysis, and then differences between distributions of efficient and inefficient units were evaluated using a Chi-square test. A Kruskal-Wallis test was also used to analyze differences in mean efficiency. Inefficiency scores were regressed with hospital characteristics to test for other determinants. These results did not confirm differences in efficiency concerning ownership. However, in some subgroups of hospitals, running an Emergency Department or an Intensive Care Unit had a significant effect. Tobit regression results provided additional insight into how an Emergency Department or Intensive Care Unit can affect efficiency. Both cases had an effect of increasing inefficiency, and the data suggested that the department/unit size plays an important role.

Variable returns to scale DEA—Taguchi approach for ternary additives optimization in expansive soil subgrade enhancement

In this study, variable returns to scale (VRS) data envelopment analysis was integrated into the Taguchi approach to optimize ternary additives for expansive soil enhancement. The ternary additives selected were sawdust ash (SDA), quarry dust (QD) and ordinary Portland cement (OPC). The additives were set as the input variables while multiple responses obtained from the experiments performed with the Taguchi orthogonal array were set as the output variables. Each row in the orthogonal array were defined as a decision making unit (DMU) in the optimization process and output-oriented VRS model was used to obtain the efficiency score for each DMU. Next, benevolent formulation was utilized to obtain the multipliers for the inputs and outputs which were subsequently used to determine the cross efficiency scores for each DMU. The cross-efficiency scores were used to construct the cross-efficiency matrix. Thereafter, the mean cross-efficiency score (MCES) was determined for each DMU. Parameter level that maximizes the MCES was chosen as the optimal level for that parameter. Optimum combination of additives was found at A6 B2 C3. Lastly, confirmatory experiments performed by blending the soil with the optimum combination of additives showed the effectiveness of this method in the enhancement of expansive soil properties.

PREDICTING THE EFFICIENCY OF COMMERCIAL BANKS IN BANGLADESH

This paper examines the efficiency of the thirty-six commercial banks (27 Domestic and 9 Foreign Banks) by reviewing Literature and analyzing 6 years (2011-2016) data. The sample was selected based on the availability of data. It is assumed that the banking sector complies with the variable returns to scale (VRS) approach which means the output of a bank is not proportionately related to its inputs. Therefore, VRS in the ‘Data Envelop Analysis (DEA)’ technique has been employed in this paper. The findings reveal that most (30 banks out of 36 banks) of the banks of Bangladesh are inefficient in terms of technical, allocative, and scale efficiency during the 2011-2016 periods. Conversely, only six banks (4 domestic banks and 2 foreign banks) were found efficient in overall scores in this scrutinization. This study did not find any single bank as efficient in all categories (allocative efficiency, technical efficiency, pure efficiency ratio) for the whole study period (2011-2016). This paper provides valuable intuition, analysis, and comments to the managers and policymakers of the bank’s efficiency score so that they comprehend their position. Finally, this paper suggests necessary steps to transform the inefficient banks into efficient banks, and to make stable the banking sector of Bangladesh. JEL Classification Codes: E51, G21, M1.

A Fuzzy Approach to Support Evaluation of Fuzzy Cross Efficiency

Cross-efficiency evaluation effectively distinguishes a set of decision-making units (DMUs) via self- and peer-evaluations. In constant returns to scale, this evaluation technique is usually applied for data envelopment analysis (DEA) models because negative efficiencies will not occur in this case. For situations of variable returns to scale, the negative cross-efficiencies may occur in this evaluation method. In the real world, the observations could be uncertain and difficult to measure precisely. The existing fuzzy cross-evaluation methods are restricted to production technologies with constant returns to scale. Generally, symmetry is a fundamental characteristic of binary relations used when modeling optimization problems. Additionally, the notion of symmetry appeared in many studies about uncertain theories employed in DEA problems, and this approach can be considered an engineering tool for supporting decision-making. This paper proposes a fuzzy cross-efficiency evaluation model with fuzzy observations under variable returns to scale. Since all possible weights of all DMUs are considered, a choice of weights is not required. Most importantly, negative cross-efficiencies are not produced. An example shows that this paper’s fuzzy cross-efficiency evaluation method has discriminative power in ranking the DMUs when observations are fuzzy numbers.

Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates

The scale of impact of the COVID-19 pandemic on society and the economy globally provides a strong incentive to thoroughly analyze the efficiency of healthcare systems in dealing with the current pandemic and to obtain lessons to prepare healthcare systems to be better prepared for future pandemics. In the absence of a proven vaccine or cure, non-pharmaceutical interventions including social distancing, testing and contact tracing, isolation, and wearing of masks are essential in the fight against the worldwide COVID-19 pandemic. We use data envelopment analysis and data compiled from Worldometers and The World Bank to analyze how efficient the use of resources were to stabilize the rate of infections and minimize death rates in the top 36 countries that represented 90% of global infections and deaths out of 220 countries as of November 11, 2020. This is the first paper to model the technical efficiency of countries in managing the COVID-19 pandemic by modeling death rates and infection rates as undesirable outputs using the approach developed by You and Yan. We find that the average efficiency of global healthcare systems in managing the pandemic is very low, with only six efficient systems out of a total of 36 under the variable returns to scale assumption. This finding suggests that, holding constant the size of their healthcare systems (because countries cannot alter the size of a healthcare system in the short run), most of the sample countries showed low levels of efficiency during this time of managing the pandemic; instead it is suspected that most countries literally “threw” resources at fighting the pandemic, thereby probably raising inefficiency through wasted resource use.

DATA ENVELOPMENT ANALYSIS (DEA) APPROACH FOR ASSESSING TECHNICAL, ECONOMIC AND SCALE EFFICIENCY OF BROILER FARMS

This study was surveyed and evaluated technical, economic and scale efficiency of broiler farms in Egypt using DEA technique. So as to accomplish the specified aim, stratified random sampling technique was utilized to gather information from 150 broiler farms. The results showed that mean technical efficiencies of broiler farms were 0.915 and 0.985 under constant returns to scale (CRS) and variable returns to scale (VRS) respectively, implying that on average the farms could reduce input utilization by 8.5% and 1.5% for production level of output to be technically efficient. Notably, 48.7% of the farms were estimated fully technical efficient under VRS-model. The mean allocative and economic efficiency of the farms were assessed as 0.941 and 0.918 respectively, with only 2% of the farms were fully allocative and economic efficient. Furthermore, the average scale efficiency was 0.929 with the majority of broiler farms (82%) were operating with increasing returns to scale. The estimated Tobit regression showed that farmer's age, education, experience, access to extension services, and level of training were the most significant variables contributing to the disparities in efficiency of broiler farms. Such results are useful for extension workers and policy makers so as to guide policies towards expanding efficiency.

Uplatnění neparametrické metody DEA při zkoumání efektivnosti obcí a měst

The article deals with the application of data envelope analysis (DEA), in examining the efficiency of selected public services provided by municipalities and cities. The method is focused on calculating indicators for individual municipalities and groups of municipalities. When calculating the efficiency, the DEA model with variable returns to scale and superefficiency is used. The distance from the efficiency limit (data envelope) is not measured by Euclidean, as classical DEA models, but by Chebyshev distance. The analysis focuses on examining efficiency within groups of municipalities, defined according to the number of inhabitants and location in relation to development centers, but also these groups in the context of the entire data set. The created model allows to calculate the efficiency of each municipality and monitor its ranking within the given category, but also the type of municipality, administrative district or region. It then shows the practical results of the calculation of efficiency - the achieved average value on the example of schools and municipal police. The variability of the results achieved is subject to interpretation with respect to the services examined. Finally, the limits of DEA use are discussed with regard to the quality of available data and the overall appropriateness of the method for monitoring the efficiency of municipalities.

Influence of Constant Returns to Scale and Variable Returns to Scale Data Envelopment Analysis Models in ICT Infrastructure Efficiency Utilization

A lot of research has been done using Data Envelopment Analysis (DEA) to measure efficiency in Education. DEA has also been used in the field of Information and Communication Technology for Development (ICT4D) to investigate and measure the efficiency of Information and Communication Technology (ICT) investments on Human Development. Education is one of the major components of the Human Development Index (HDI) which affects the core of Human Development. This research investigates the relative efficiency of ICT Infrastructure Utilization on the educational component of the HDI in order to determine the viability of Learning Analytics using DEA for policy direction and decision making. A conceptual model taking the form of a Linear Equation was used and the Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) models of the Data Envelopment Analysis were employed to measure the relative efficiency of the components of ICT Infrastructure (Inputs) and the components of Education (Outputs). Results show a generally high relative efficiency of ICT Infrastructure utilization on Educational Attainment and Adult Literacy rates, a strong correlation between this Infrastructure and Literacy rates as well, provide an empirical support for the argument of increasing ICT infrastructure to provide an increase in Human Development, especially within the educational context. The research concludes that DEA as a methodology can be used for macroeconomic decision making and policy direction within developmental research.